Comparative study on adaptive rational macromodels of highly resonant structures

Two macromodeling techniques using rational bases are investigated to accurately predict the natural frequencies of highly resonant microwave structures. Three methods are proposed and compared to calculate pole‐free solutions to the Thiele continued fraction and vector fitting pole‐residue models of the characteristic equation det[Z(s)]. © 2007 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2007.     

用傅里叶变换计算单缝衍射的光强分布

现代光学和通讯理论由于引入了傅里叶变换的方法，使计算光学系统和通讯系统的物像的对应关系简洁、清晰。该文用傅氏变换计算长为２ａ、照度为Ａ的均匀缝光源在观察面上的光强分布为Ｉ（ｘ）＝│Ｅ（ｘ）│＾２＝ＡＡ＾２ａ＾２（ｓｉｎ２πｆａ／２πｆａ）此方法也可运用于矩形孔、圆孔等。     

SVPWM三相电压型逆变器的仿真研究

以三相电压型逆变器为基础,根据空间矢量脉宽调制（SVPWM）基本原理详细分析研究了电压矢量作用时间和扇区分配问题,以α-β坐标为基础,建立了互差π/3的x-y-z时间坐标系,提出了不同扇区矢量作用时间长短根据期望极值判断的原则。介绍了MATLAB中M文件编程的仿真方法,建立了MATLAB程序嵌入式主要功能模块,逆变器采用数字PID控制,利用Simulink对系统进行了综合仿真分析及试验,给出了仿真及实验波形,为实现SVPWM逆变器的数字控制提供参考。     

无线ILC系统测控信号延时效应补偿方法

针对通过无线信道进行测控信号传输的远程迭代学习控制( ILC)系统,研究信号传输延时对控制系统性能的影响.分析了测控信号延时情况下ILC系统的收敛性问题,通过引入预控制向量,提出了ILC系统信道延时效应补偿的向量更新方法.数值仿真结果表明了所提方法的有效性.     

一种新的码片内多径时延估计方法

根据信道冲激响应的稀疏特性,提出了一种频域的时延估计压缩感知模型,将时延估计问题转化为基于欠采样数据的稀疏向量估计问题.利用离散傅里叶变换(Discrete Fourier transform,DFT)矩阵的子矩阵所满足的受限等距性(Restricted isometry property,RIP)以及信道冲激响应的稀疏特性充分降低了时延估计所需数据量的要求.分析了本文模型具有码片内多径分辨能力以及良好抗噪性能的原因,并与多信号分类(Multiple signal classification,MUSIC)和旋转不变技术的信号参数估计(Estimation of signal parameters via rotational invariance technique,ESPRIT)算法的时延估计性能进行仿真比较.仿真结果表明,本文提出的方法不需要预知多径的条数,对码片内多径时延具有较高的估计精度,其时延估计性能在特定条件下优于MUSIC和ESPRIT算法.     

Entropy/cross entropy-based group decision making under intuitionistic fuzzy environment

We study the group decision making problem under intuitionistic fuzzy environment. Based on entropy and cross entropy, we give two methods to determine the optimal weights of attributes, and develop two pairs of entropy and cross entropy measures for intuitionistic fuzzy values. Then, we discuss the properties of these measures and the relations between them and the existing ones. Furthermore, we introduce three new aggregation operators, which treat the membership and non-membership information fairly, to aggregate intuitionistic fuzzy information. Finally, several practical examples are presented to illustrate the developed methods.     

A hybrid stock selection model using genetic algorithms and support vector regression

In the areas of investment research and applications, feasible quantitative models include methodologies stemming from soft computing for prediction of financial time series, multi-objective optimization of investment return and risk reduction, as well as selection of investment instruments for portfolio management based on asset ranking using a variety of input variables and historical data, etc. Among all these, stock selection has long been identified as a challenging and important task. This line of research is highly contingent upon reliable stock ranking for successful portfolio construction. Recent advances in machine learning and data mining are leading to significant opportunities to solve these problems more effectively. In this study, we aim at developing a methodology for effective stock selection using support vector regression (SVR) as well as genetic algorithms (GAs). We first employ the SVR method to generate surrogates for actual stock returns that in turn serve to provide reliable rankings of stocks. Top-ranked stocks can thus be selected to form a portfolio. On top of this model, the GA is employed for the optimization of model parameters, and feature selection to acquire optimal subsets of input variables to the SVR model. We will show that the investment returns provided by our proposed methodology significantly outperform the benchmark. Based upon these promising results, we expect this hybrid GA-SVR methodology to advance the research in soft computing for finance and provide an effective solution to stock selection in practice.     

Soft computing approach to fault diagnosis of centrifugal pump

Fault detection and isolation in rotating machinery is very important from an industrial viewpoint as it can help in maintenance activities and significantly reduce the down-time of the machine, resulting in major cost savings. Traditional methods have been found to be not very accurate. Soft computing based methods are now being increasingly employed for the purpose. The proposed method is based on a genetic programming technique which is known as gene expression programming (GEP). GEP is somewhat a new member of the genetic programming family. The main objective of this paper is to compare the classification accuracy of the proposed evolutionary computing based method with other pattern classification approaches such as support vector machine (SVM), Wavelet-GEP, and proximal support vector machine (PSVM). For this purpose, six states viz., normal, bearing fault, impeller fault, seal fault, impeller and bearing fault together, cavitation are simulated on centrifugal pump. Decision tree algorithm is used to select the features. The results obtained using GEP is compared with the performance of Wavelet-GEP, support vector machine (SVM) and proximal support vector machine (PSVM) based classifiers. It is observed that both GEP and SVM equally outperform the other two classifiers (PSVM and Wavelet-GEP) considered in the present study.     

压力容器CAD绘图软件包的研究

本文结合压力容器计算机辅助设计软件的研制,研究了利用AUTOCAD开发计算机辅助设计专用软件的几个关键性问题;主要有程序绘图,汉字信息、绘图数据的显示和选择以及与绘图程序的连接、专用软件的控制结构设计和内存管理等问题;提出了一整套设计新颖,使用方便,运行高效的解决方法。     

A parallel Monte Carlo search algorithm for the conformational analysis of polypeptides

In recent years several approaches have been proposed to overcome the multiple-minima problem associated with nonlinear optimization techniques used in the analysis of molecular conformations. One such technique based on a parallel Monte Carlo search algorithm is analyzed. Experiments on the Intel iPSC/2 confirm that the attainable parallelism is limited by the underlying acceptance rate in the Monte Carlo search. It is proposed that optimal performance can be achieved in combination with vector processing. Tests on both the IBM 3090 and Intel iPSC/2-VX indicate that vector performance is related to molecule size and vector pipeline latency.